{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supervised methods\n",
    "Since we now have labeled data, we can try some supervised methods. Our stakeholders are looking for a model with recall of at least 75% and precision of at least 95%.\n",
    "\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sqlite3\n",
    "\n",
    "with sqlite3.connect('logs/logs.db') as conn:\n",
    "    logs_2018 = pd.read_sql(\n",
    "        'SELECT * FROM logs WHERE datetime BETWEEN \"2018-01-01\" AND \"2019-01-01\";', \n",
    "        conn, parse_dates=['datetime'], index_col='datetime'\n",
    "    )\n",
    "    hackers_2018 = pd.read_sql(\n",
    "        'SELECT * FROM attacks WHERE start BETWEEN \"2018-01-01\" AND \"2019-01-01\";', \n",
    "        conn, parse_dates=['start', 'end']\n",
    "    ).assign(\n",
    "        duration=lambda x: x.end - x.start, \n",
    "        start_floor=lambda x: x.start.dt.floor('min'),\n",
    "        end_ceil=lambda x: x.end.dt.ceil('min')\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_X(log, day):\n",
    "    \"\"\"\n",
    "    Get data we can use for the X\n",
    "    \n",
    "    Parameters:\n",
    "        - log: The logs dataframe\n",
    "        - day: A day or single value we can use as a datetime index slice\n",
    "    \n",
    "    Returns: \n",
    "        A pandas DataFrame\n",
    "    \"\"\"\n",
    "    return pd.get_dummies(log[day].assign(\n",
    "        failures=lambda x: 1 - x.success\n",
    "    ).query('failures > 0').resample('1min').agg(\n",
    "        {'username':'nunique', 'failures': 'sum'}\n",
    "    ).dropna().rename(\n",
    "        columns={'username':'usernames_with_failures'}\n",
    "    ).assign(\n",
    "        day_of_week=lambda x: x.index.dayofweek, \n",
    "        hour=lambda x: x.index.hour\n",
    "    ).drop(columns=['failures']), columns=['day_of_week', 'hour'])\n",
    "\n",
    "def get_y(datetimes, hackers, resolution='1min'):\n",
    "    \"\"\"\n",
    "    Get data we can use for the y (whether or not a hacker attempted a log in during that time).\n",
    "    \n",
    "    Parameters:\n",
    "        - datetimes: The datetimes to check for hackers\n",
    "        - hackers: The dataframe indicating when the attacks started and stopped\n",
    "        - resolution: The granularity of the datetime. Default is 1 minute.\n",
    "        \n",
    "    Returns:\n",
    "        A pandas Series of booleans.\n",
    "    \"\"\"\n",
    "    date_ranges = hackers.apply(\n",
    "        lambda x: pd.date_range(x.start_floor, x.end_ceil, freq=resolution), \n",
    "        axis=1\n",
    "    )\n",
    "    dates = pd.Series()\n",
    "    for date_range in date_ranges:\n",
    "        dates = pd.concat([dates, date_range.to_series()])\n",
    "    return datetimes.isin(dates)\n",
    "\n",
    "def get_X_y(log, day, hackers):\n",
    "    \"\"\"\n",
    "    Get the X, y data to build a model with.\n",
    "    \n",
    "    Parameters:\n",
    "        - log: The logs dataframe\n",
    "        - day: A day or single value we can use as a datetime index slice\n",
    "        - hackers: The dataframe indicating when the attacks started and stopped\n",
    "        \n",
    "    Returns:\n",
    "        X, y tuple where X is a pandas DataFrame and y is a pandas Series\n",
    "    \"\"\"\n",
    "    X = get_X(log, day)\n",
    "    y = get_y(X.reset_index().datetime, hackers)\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will train on January 2018 and test on February 2018:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = get_X_y(logs_2018, '2018-01', hackers_2018)\n",
    "X_test, y_test = get_X_y(logs_2018, '2018-02', hackers_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised anomaly detection\n",
    "Now that we have our labeled data, let's use supervised learning algorithms to see if we can use this extra information to find the hackers. We will build two baseline models first:\n",
    "\n",
    "- a dummy classifier model that will predict labels based on the stratification in the data\n",
    "- a Naive Bayes model that will predict the labels using Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baselining\n",
    "#### Dummy classifier\n",
    "This classifier will give us a model that is equivalent to the baseline we have been drawing on our ROC curves. The results will be poor on purpose. We will never use this classifier to actually make predictions--rather, we can use it to see if the models we are building are better than random guessing strategies. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyClassifier # build a baseline\n",
    "\n",
    "dummy_model = DummyClassifier(strategy='stratified', random_state=0).fit(X_train, y_train)\n",
    "dummy_preds = dummy_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Evaluation Partials\n",
    "Create partials for less typing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create partials for less typing\n",
    "from functools import partial\n",
    "from sklearn.metrics import classification_report\n",
    "from ml_utils.classification import confusion_matrix_visual, plot_pr_curve, plot_roc\n",
    "\n",
    "report = partial(classification_report, y_test)\n",
    "roc = partial(plot_roc, y_test)\n",
    "pr_curve = partial(plot_pr_curve, y_test)\n",
    "conf_matrix = partial(confusion_matrix_visual, y_test, class_labels=[False, True])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No attackers are properly flagged with the dummy model. Notice how the ROC curve is diagonal and on top of the baseline we have seen in previous chapters. The precision-recall curve is also along its baseline at the percentage of attackers in the data. This means the dummy model is equivalent to random guessing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Dummy Classifier with Stratified Strategy')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(20, 5))\n",
    "roc(dummy_model.predict_proba(X_test)[:,1], ax=axes[0])\n",
    "conf_matrix(dummy_preds, ax=axes[1])\n",
    "pr_curve(dummy_model.predict_proba(X_test)[:,1], ax=axes[2])\n",
    "plt.suptitle('Dummy Classifier with Stratified Strategy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dummy classifier has 0 precision, recall, and F1 score for the positive class (attacker):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       1.00      1.00      1.00     37787\n",
      "        True       0.00      0.00      0.00        16\n",
      "\n",
      "   micro avg       1.00      1.00      1.00     37803\n",
      "   macro avg       0.50      0.50      0.50     37803\n",
      "weighted avg       1.00      1.00      1.00     37803\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(report(dummy_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Naive Bayes\n",
    "Naive Bayes makes a naive assumption of conditional independence of the features to simplify the use of Bayes theorem for modeling. It is very fast to train and a good baseline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\base.py:467: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, y, **fit_params).transform(X)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:331: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB # build another baseline with Naive Bayes\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "nb_pipeline = Pipeline([\n",
    "    ('scale', StandardScaler()),\n",
    "    ('nb', GaussianNB())\n",
    "]).fit(X_train, y_train)\n",
    "nb_preds = nb_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The prior probabilities will be the percentage of each class in the data. 99.93% for valid users and 0.07% for attackers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.99303169e-01, 6.96830622e-04])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_pipeline.named_steps['nb'].class_prior_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model correctly flags a few of the attackers, but it has a ton of false positives. Note that this is better than the dummy model, but not by much:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Naive Bayes Classifier')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(20, 5))\n",
    "roc(nb_pipeline.predict_proba(X_test)[:,1], ax=axes[0])\n",
    "conf_matrix(nb_preds, ax=axes[1])\n",
    "pr_curve(nb_pipeline.predict_proba(X_test)[:,1], ax=axes[2])\n",
    "plt.suptitle('Naive Bayes Classifier')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is better than the dummy classifier, but it doesn't meet our stakeholders' requirements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       1.00      0.80      0.89     37787\n",
      "        True       0.00      0.38      0.00        16\n",
      "\n",
      "   micro avg       0.80      0.80      0.80     37803\n",
      "   macro avg       0.50      0.59      0.44     37803\n",
      "weighted avg       1.00      0.80      0.89     37803\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(report(nb_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression\n",
    "We will be maximizing recall (averaged across the classes):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "lr_pipeline = Pipeline([\n",
    "    ('scale', StandardScaler()),\n",
    "    ('lr', LogisticRegression(solver='lbfgs', random_state=0))\n",
    "])\n",
    "\n",
    "search_space = {\n",
    "    'lr__C' : [0.1, 0.5, 1, 2]\n",
    "}\n",
    "\n",
    "lr_grid = GridSearchCV(\n",
    "    lr_pipeline, search_space, scoring='recall_macro', cv=5\n",
    ").fit(X_train, y_train)\n",
    "\n",
    "lr_preds = lr_grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Say we knew how much our stakeholders valued recall over precision. We could then make a custom F-beta scorer to use with grid search. Here is an scorer that values recall 5 times more than precision:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import make_scorer, fbeta_score\n",
    "f_five = make_scorer(fbeta_score, beta=5) # pass as the `scoring` argument to grid search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The regularization selected from the grid search is the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lr__C': 0.5}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model had no false positives and is much better than the baselines. The ROC curve is much closer to the top left corner, and the precision-recall is much closer to the top right corner. Notice that the ROC curve is a bit more optimistic of the performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Logistic Regression Classifier')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(20, 5))\n",
    "roc(lr_grid.predict_proba(X_test)[:,1], ax=axes[0])\n",
    "conf_matrix(lr_preds, ax=axes[1])\n",
    "pr_curve(lr_grid.predict_proba(X_test)[:,1], ax=axes[2])\n",
    "plt.suptitle('Logistic Regression Classifier')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model meets the requirements of the SOC. Our recall is at least 75% and precision is at least 95%:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       1.00      1.00      1.00     37787\n",
      "        True       1.00      0.75      0.86        16\n",
      "\n",
      "   micro avg       1.00      1.00      1.00     37803\n",
      "   macro avg       1.00      0.88      0.93     37803\n",
      "weighted avg       1.00      1.00      1.00     37803\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(report(lr_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, there is a problem. We can't update this model with additional labeled data. It has to be trained from scratch to add data. We will see how to support this in the next notebook on [online learning](https://github.com/stefmolin/Hands-On-Data-Analysis-with-Pandas/blob/master/ch_11/5-online_learning.ipynb)."
   ]
  }
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